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用于早期准确预测血流动力学损害的代偿储备:急性护理和身体表现临床应用的案例研究

Compensatory Reserve for Early and Accurate Prediction of Hemodynamic Compromise: Case Studies for Clinical Utility in Acute Care and Physical Performance.

作者信息

Stewart Camille L, Nawn Corinne D, Mulligan Jane, Grudic Greg, Moulton Steven L, Convertino Victor A

出版信息

J Spec Oper Med. 2016 Spring;16(1):6-13.

Abstract

BACKGROUND

Humans are able to compensate for significant loss of their circulating blood volume, allowing vital signs to remain relatively stable until compensatory mechanisms are overwhelmed. The authors present several clinical and performance case studies in an effort to demonstrate real-time measurements of an individual's reserve to compensate for acute changes in circulating blood volume. This measurement is referred to as the Compensatory Reserve Index (CRI).

METHODS

We identified seven clinical and two physical performance conditions relevant to military casualty and operational medicine as models of intravascular volume compromise. Retrospective analysis of photoplethysmogram (PPG) waveform features was used to calculate CRI, where 1 represents supine normovolemia and 0 represents hemodynamic decompensation.

RESULTS

All cases had CRI values suggestive of volume compromise (<0.6) not otherwise evident by heart rate and systolic blood pressure. CRI decreased with reduced central blood volume and increased with restored volume (e.g., fluid resuscitation).

CONCLUSION

The results from these case studies demonstrate that machine-learning techniques can be used to (1) identify a clinical or physiologic status of individuals through real-time measures of changes in PPG waveform features that result from compromise to circulating blood volume and (2) signal progression toward hemodynamic instability, with opportunity for early and effective intervention, well in advance of changes in traditional vital signs.

摘要

背景

人类能够代偿循环血容量的显著损失,使生命体征在代偿机制不堪重负之前保持相对稳定。作者展示了几个临床和性能案例研究,以努力证明对个体代偿循环血容量急性变化储备的实时测量。这种测量被称为代偿储备指数(CRI)。

方法

我们确定了七种与军事伤员和作战医学相关的临床和两种身体性能状况,作为血管内容量受损的模型。采用回顾性分析光电容积脉搏波(PPG)波形特征来计算CRI,其中1代表仰卧位血容量正常,0代表血流动力学失代偿。

结果

所有病例的CRI值均提示血容量受损(<0.6),而心率和收缩压并无其他明显表现。CRI随着中心血容量减少而降低,随着血容量恢复(如液体复苏)而升高。

结论

这些案例研究的结果表明,机器学习技术可用于:(1)通过实时测量因循环血容量受损导致的PPG波形特征变化来识别个体的临床或生理状态;(2)在传统生命体征出现变化之前,很早就发出向血流动力学不稳定进展的信号,从而有机会进行早期有效干预。

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